28
Industrial ecology methods have reached high levels of sophistication and are
broadly applied in companies and academia alike, but their use for prospective
assessment of transformation strategies has remained a niche application.
Prospective scenario exercises for I/O tables have repeatedly been conducted over
the last decades (Cantono et al. 2008 ; De Koning et al. 2015 ; de Lange 1980 ;
Idenburg and Wilting 2000 ; Leontief and Duchin 1986 ; Levine et al. 2007 ), but this
modeling approach has not entered mainstream research on society’s future metabolism. The reason may be twofold: (1) constructing I/O tables for future years
requires many assumptions to be made and (2) using I/O tables in monetary units to
measure interindustry fl ows, as in the studies above, makes it diffi cult to include
physical process descriptions for specifi c technologies. Beyond IE, I/O tables form
the core of computable general equilibrium (CGE) and prospective econometric
models like the E3ME model (Burfi sher 2011 ; Cambridge Econometrics 2014 ).
Most LCA studies are retrospective and attributional ; they use historic data to
model the life cycle of product systems and provide timeless indicators for environmental product performance. Prospective LCA (Lundie et al. 2004 ; Spielmann et al.
2005 ) and consequential LCA (CLCA) (Earles and Halog 2011 ; Finnveden et al.
2009 ; Whitefoot et al. 2011 ) add a forward-looking perspective to LCA. They typically assess transformation strategies on the small scale.
Prospective MFA studies mostly cover metals and building materials but do not
include other layers or satellite accounts (Elshkaki and Graedel 2013 ; Hatayama
et al. 2010 ; D. B. Müller 2006 ; Northey et al. 2014 ; Pauliuk et al. 2012 ; Sartori et al.
2008 ; Gallardo et al. 2014 ).
From the methods above, only MFA has been used to analyze preindustrial societies’ socio-metabolic transitions (Krausmann 2011 ; Schaffartzik et al. 2014 ;
Sieferle et al. 2006 ). These studies quantifi ed trends in the total energy and material
turnover of different socio-metabolic regimes, but they did not assess specifi c transformation strategies to shift from one regime to another.
3.2 New Approaches to Prospective Modeling in Industrial
Ecology
The state of development of the above methods to conduct prospective studies of the
next socio-metabolic transition is not satisfactory. The history of IE exhibits several
examples for problems that were overcome by combining different IE methods into
new frameworks. Examples include hybrid LCA (Suh et al. 2004 ) and WIO- MFA
(Nakamura et al. 2007 ).
To come closer to the ultimate goal of studying a wide spectrum of transformation
strategies at full scale in a common prospective modeling framework, the established
IE methods have been combined in novel ways. As a result, a new family of prospective industrial ecology models is available, and we briefl y present two of its members,
extended dynamic MFA and THEMIS (Technology-Hybridized EnvironmentalEconomic Model with Integrated Scenarios), and their application so far.
S. Pauliuk and E.G. Hertwich
Industrial ecology methods have reached high levels of sophistication and are
broadly applied in companies and academia alike, but their use for prospective
assessment of transformation strategies has remained a niche application.
Prospective scenario exercises for I/O tables have repeatedly been conducted over
the last decades (Cantono et al. 2008 ; De Koning et al. 2015 ; de Lange 1980 ;
Idenburg and Wilting 2000 ; Leontief and Duchin 1986 ; Levine et al. 2007 ), but this
modeling approach has not entered mainstream research on society’s future metabolism. The reason may be twofold: (1) constructing I/O tables for future years
requires many assumptions to be made and (2) using I/O tables in monetary units to
measure interindustry fl ows, as in the studies above, makes it diffi cult to include
physical process descriptions for specifi c technologies. Beyond IE, I/O tables form
the core of computable general equilibrium (CGE) and prospective econometric
models like the E3ME model (Burfi sher 2011 ; Cambridge Econometrics 2014 ).
Most LCA studies are retrospective and attributional ; they use historic data to
model the life cycle of product systems and provide timeless indicators for environmental product performance. Prospective LCA (Lundie et al. 2004 ; Spielmann et al.
2005 ) and consequential LCA (CLCA) (Earles and Halog 2011 ; Finnveden et al.
2009 ; Whitefoot et al. 2011 ) add a forward-looking perspective to LCA. They typically assess transformation strategies on the small scale.
Prospective MFA studies mostly cover metals and building materials but do not
include other layers or satellite accounts (Elshkaki and Graedel 2013 ; Hatayama
et al. 2010 ; D. B. Müller 2006 ; Northey et al. 2014 ; Pauliuk et al. 2012 ; Sartori et al.
2008 ; Gallardo et al. 2014 ).
From the methods above, only MFA has been used to analyze preindustrial societies’ socio-metabolic transitions (Krausmann 2011 ; Schaffartzik et al. 2014 ;
Sieferle et al. 2006 ). These studies quantifi ed trends in the total energy and material
turnover of different socio-metabolic regimes, but they did not assess specifi c transformation strategies to shift from one regime to another.
3.2 New Approaches to Prospective Modeling in Industrial
Ecology
The state of development of the above methods to conduct prospective studies of the
next socio-metabolic transition is not satisfactory. The history of IE exhibits several
examples for problems that were overcome by combining different IE methods into
new frameworks. Examples include hybrid LCA (Suh et al. 2004 ) and WIO- MFA
(Nakamura et al. 2007 ).
To come closer to the ultimate goal of studying a wide spectrum of transformation
strategies at full scale in a common prospective modeling framework, the established
IE methods have been combined in novel ways. As a result, a new family of prospective industrial ecology models is available, and we briefl y present two of its members,
extended dynamic MFA and THEMIS (Technology-Hybridized EnvironmentalEconomic Model with Integrated Scenarios), and their application so far.
S. Pauliuk and E.G. Hertwich